What is Emotion Analysis?
Many businesses in today’s time make use of sentiment analysis to determine customers’ perceptions of their business and understand their true feelings. However, sometimes sentiment analysis fails, and this is where emotion analysis comes into play. If a business does not understand its customers’ true feelings and mindset, it defeats the purpose of sentiment analysis.
Understanding the sentiments of customers is important to offer a good customer experience, and emotion analysis can help with this. Businesses can use it to know about the basic emotions expressed by their customers in reviews and feedback.
Emotion analysis is executed based on the feelings shown in a text, like anger, fear, happiness, inspiration, love, sadness, etc. To determine the overall feel of customers and followers, a business can gather large feedback text data and perform emotion detection.
Emotion Analysis – What Is It?
The emotional analysis is the technique used to discover and interpret underlying emotions in a piece of text. It involves gathering data from various resources to examine subjective information and discover the true sentiments behind them.
Emotion analysis, detection, and classification depend entirely on the basic expressions of emotion communicated in a text. These feelings range from love, sadness, inspiration, happiness, rage, fear, neutrality, etc. The main intent is to use natural language processing to analyze text by extracting ideas, views, and thoughts and assigning them neutral, positive, or negative polarity.
Most customer reviews feature information and their feelings about product purchases and other services. Businesses can perform emotion analysis on these ratings and reviews to ensure better decision-making and improve their overall operations.
Emotion Analysis Vs. Sentiment Analysis
Both these types help businesses manage their brand reputation and improve customer experience as they understand what their users want and don’t like. They offer insights like what gender and age prefer a particular show, most and least popular products, feelings that spike over the night, positive, negative, etc.
Sentiment analysis feature about the general human feelings that a viewer experiences. It does not focus on basic or complex feelings, just on contrasting negative and positive expressions of feelings.
These are the different types of sentiment analysis-
Rule or aspect-based sentiment analysis – This type performs sentiment analysis based on a deep learning technique and a set of rules. It depends on an elaborate knowledge lexicon and different languages.
Automatic sentiment analysis – This type relies on machine learning approaches for analysis and processes text automatically.
Hybrid sentiment analysis – This type combines both machine learning and deep learning models.
Emotional detection, on the other hand, is a complex system for understanding text responses. Instead of just positive or negative sentiments, it focuses on a wide range of human feelings. It can measure the difference in feelings expressed by consumers through text and emojis.
It also includes the impulses and motives of the viewer and helps translate the results into actions. Happy or sad sentiments show that a consumer likes or does not like a product. A confusion matrix shows that a product is complicated and customers do not understand how to use it. Boring feelings show that a business needs to do something fresh, creative, and engaging.
Benefits of Using Emotion Analysis
When it comes to accuracy and understanding every deep layer of human feelings, emotion analysis outperforms sentiment analysis. Here are the benefits of using it-
- It examines and determines the various sentiments communicated in a piece of text by analyzing large amounts of customer feedback and social media data.
- It helps determine brand reputation as perceived by consumers and notifies them about things that could degrade a business’ reputation.
- It helps in identifying serious problems at an early stage before they turn into something serious.
Uses of Emotion Analysis
Different businesses already utilize complex emotion recognition extensively to determine how customers feel about their goods, brands, marketing initiatives, employees, or on-site interactions.
Understanding client emotions is essential for business growth and experience improvement, but this technology offers options that go beyond market research and digital advertising. So, let’s have a look at them:
Predict and Recognize Market Trends
You can analyze large networks of market research data to know about emerging trends and better understand customers’ positive sentiment. This helps in learning and improving business operations based on what is in demand and navigating the complex stock market world. In addition, the prediction of market trends helps in better business decision-making.
Analyze Political and Public Opinion Polls
Emotion analysis helps collect and analyze the vast amount of text, like news, suggestions, opinions, reviews, polls, surveys, etc., related to political elections. You can understand how the public perceives candidates and who can win the election.
Maintain a Check Over Brand Image
Businesses can determine customers’ feelings towards various services and products. It even helps in product analysis and provides relevant data needed to make improvisations in the product. This data will further help in enhancing customer service and attracting more audiences to your business.
Analyze Customer Feedback Data
Businesses can analyze customer feedback data and look at areas where they can improve things. You can understand what your customers don’t like and what they expect from you. All this value and insights from the feedback help offer a better customer experience and satisfaction.
Analyze Social Media Conversations
Many customers speak their hearts out and discuss a brand on social media. You can analyze these conversations to determine your customers’ sentiments towards your brand and what they really feel. You can improve marketing strategies and product planning with this data.
You may quickly sift through all of that data to examine user sentiment across all social media platforms as well as specific user emotions. The emotional analysis enables you to read beyond simple definitions to recognize sarcasm, understand popular chat acronyms (lol, rofl, etc.), and correct grammatical and spelling errors that are frequently made.
Minimize Employee Turnover
Businesses can determine employee satisfaction levels by analyzing employee feedback data. It helps in motivating employees that don’t feel great. These insights also help boost employee productivity and morale so that they don’t quit their jobs.
For business success and to formulate the best marketing strategies, it is important to track and understand your customers’ true feelings. Customers who had a positive and happy experience with a business are 15 times more likely to recommend it forward than those who had a bad, negative experience.
Sentiment analysis and emotional analysis both serve a similar networking, but it is safe to say that emotion analysis is the better, more accurate, and more effective option. Sentiment analysis tells about neutral, positive, or negative sentiments in a text, but emotion analysis tells you about the true feelings and motivations behind a text.
We recommend every business to use this technique if they want to really understand the needs and demands of their customers and take their business to new heights.